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Assessment of Offspring DNA Methylation across the Lifecourse Associated with Prenatal Maternal Smoking Using
Frank de Vocht1, Andrew J Simpkin2,3, Rebecca C Richmond4
1School of Social and Community Medicine, University of Bristol, Bristol BS8 2PS, UK. frank.devocht@bristol.ac.uk.
Insights
Maternal smoking during pregnancy can affect child health through DNA methylation changes. A new Bayesian model reveals sustained effects on offspring DNA methylation, confirming links to genes like MYO1G and GFI1.
Area of Science:
- Epigenetics
- Developmental Biology
- Environmental Health
Background:
- Maternal smoking in pregnancy is linked to offspring ill-health.
- DNA methylation is a potential mediator of these effects.
- Analyzing genome-wide DNA methylation data presents challenges due to CpG site correlations.
Purpose of the Study:
- To investigate the longitudinal effects of maternal smoking in pregnancy on offspring DNA methylation.
- To apply a Bayesian hierarchical mixture model to analyze correlated DNA methylation data, avoiding data reduction methods.
Main Methods:
- Utilized longitudinal DNA methylation data from a UK birth cohort (birth, age 7, 17).
- Employed a Bayesian hierarchical mixture model to analyze genome-wide DNA methylation patterns.
- Investigated associations between maternal smoking and offspring DNA methylation at CpG sites.
Main Results:
- Replicated associations between maternal smoking and offspring DNA methylation at 4 previously identified smoking-related CpG sites.
- Confirmed findings in well-known smoking-related genes MYO1G and GFI1.
- Identified further weak associations at the AHRR and CYP1A1 loci.
Conclusions:
- The Bayesian hierarchical mixture model is effective for analyzing longitudinal DNA methylation data.
- This method offers an alternative to data reduction techniques in epigenome-wide association studies.
- The findings reinforce the role of DNA methylation in mediating the long-term health impacts of maternal smoking.
Abstract:
A growing body of research has implicated DNA methylation as a potential mediator of the effects of maternal smoking in pregnancy on offspring ill-health. Data were available from a UK birth cohort of children with DNA methylation measured at birth, age 7 and 17. One issue when analysing genome-wide DNA methylation data is the correlation of methylation levels between CpG sites, though this can be crudely bypassed using a data reduction method. In this manuscript we investigate the effect of sustained maternal smoking in pregnancy on longitudinal DNA methylation in their offspring using a Bayesian hierarchical mixture model. This model avoids the data reduction used in previous analyses. Four of the 28 previously identified, smoking related CpG sites were shown to have offspring methylation related to maternal smoking using this method, replicating findings in well-known smoking related genes MYO1G and GFI1. Further weak associations were found at the AHRR and CYP1A1 loci. In conclusion, we have demonstrated the utility of the Bayesian mixture model method for investigation of longitudinal DNA methylation data and this method should be considered for use in whole genome applications.
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